Palantir Technologies Inc.

Duncan Hoffman

Harkirat Singh

Gautam Punukollu

Salar Al Khafaji

Matthew Julius Wilson

Nicolas Prettejohn

Pedro Sanzovo

Alexander Maass

Stephen Heitkamp

Richard Deitch

Maciej Biskupiak

Jorien Van Den Bergh

Juan Jimenez Puig

Ben Regev

Eugene Mak

Rootul Patel

Marissa Miracolo

Satej Soman

Elise Norman

Patrick Walter

Elizabeth Patitucci

Yevhen Shevchuk

Jakub Kowalik

Joshua Aschheim

Kai Kamberger

A customer risk trigger associated with a customer may be identified. A response to the customer risk trigger may be detected. First risk analysis data related to the customer risk trigger may be gathered, based on the response, from a first datastore. Second risk analysis data related to the customer risk trigger may be gathered, based on the response, from a second datastore. A customer risk profile to model risk attribute(s) of the customer may be gathered. The risk attributes may represent a risk correlation between the customer and a prohibited act. Customer risk visualization tool(s) configured to facilitate visual user interaction with the customer risk profile may be gathered. The customer risk visualization tools may be rendered in a display of the computing system. The customer risk visualization tools provide a customer-centric view of risk for various applications, including anti-money laundering applications.

Automated risk visualization using customer-centric data analysis

Computer-implemented systems and methods are disclosed for automatically aggregating, analyzing, and presenting probabilities associated with data items. Data items may be associated with probabilities or risks, and the data items may have various characteristics. A grouping of data items may be determined based on these characteristics, and probabilities within groups of data items may be aggregated and analyzed. Aggregated probabilities may be used to determine incremental probabilities for individual data items, to assess cumulative risk associated with a group of data items, and to analyze probabilities associated with a particular data item group. User interfaces may be generated to facilitate selection and grouping of data items, selection of risk models, and analysis of aggregate probabilities.

Data item aggregate probability analysis system

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New York, NY, United States of America

Duncan Hoffman